mar'18 | Two papers accepted for ICLR Workshops: - DeepNCM: Deep Nearest Class Mean Classifiers, with Samantha Guerriero and Barbara Caputo, see openreview and the code on GitHub - IterGANs for Object Rotation, with Ysbrand Galama, see openreview, the extended version on arXiv, and source code on GitHub.

After completion, the supervisor and examinator will grade your project and hand in the grade at the ESC desk.Note (since Jan 2019):In the current approval form an assesment form is attached. When you have an older form, download the new form and use the provided grading/assessment form.

Note: In total you're entitled to have a maximum of 12 ECTS of project courses (PAI, PAI2, and PAI3) on your study programme!

Teaching & Supervision

Open Projects

MSc AI thesis

Encoding Context in Visual RepresentationsAbstract: The goal of this project is to study how context is encoded in visual representations.
There is evidence that activation patterns in brain regions related to the visual observations are influenced by an external context, eg by music.
So, depending on the style of music, the same visual stimulus yield different activation patterns.
In this project, we aim to reproduce this behavior by training ConvNets with an additional context input.
The goal is to observe to what extend we obtain similar results and how those insights can be used for further understanding of the brain.

This is a joint project with Jorrit Montijn (Netherlands Institute for Neuroscience, Google Scholar).

Prior knowledge:

Followed Computer Vision 1 / Deep Learning

Interest in Machine Learning

Interest in Neuroscience

Willing to work at both NIN and Science Park

Experience with TensorFlow/PyTorch

Large Scale Visual Classification Using Class MeansAbstract: The final layer of (almost) any classification network is a soft-max layer.
An alternative is to learn using class prototypes, eg the mean representation of each class.
In this project we explore Deep Nearest Mean Classification for large scale classification.
The starting point is the DeepNCM paper, but the goal is to extend DeepNCM and to explore training on large(r) datasets in the order of 3,000 classes and 10M training images.